• Journal of applied mathematics & informatics
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• 제28권3_4호
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• pp.687-696
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• 2010

In this paper, an algorithm for computing the sparse approximate inverse factor of matrix $A^{T}\;A$, where A is an $m\;{\times}\;n$ matrix with $m\;{\geq}\;n$ and rank(A) = n, is proposed. The computation of the inverse factor are done without computing the matrix $A^{T}\;A$. The computed sparse approximate inverse factor is applied as a preconditioner for solving normal equations in conjunction with the CGNR algorithm. Some numerical experiments on test matrices are presented to show the efficiency of the method. A comparison with some available methods is also included.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1997년도 추계학술발표회논문집(1)
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• pp.85-90
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• 1997

Previous our numerical results in computing point kinetics equations show a possibility in developing approximations to estimate sensitivity responses of nuclear reactor We recalculate sensitivity responses by maintaining the corrections with first order of sensitivity parameter. We present a method for computing sensitivity responses of nuclear reactor based on an approximation derived from point kinetics equations. Exploiting this approximation, we found that the first order approximation works to estimate variations in the time to reach peak power because of their linear dependence on a sensitivity parameter, and that there are errors in estimating the peak power in the first order approximation for larger sensitivity parameters. To confirm legitimacy of our approximation, these approximate results are compared with exact results obtained from our previous numerical study.

• 대한기계학회논문집A
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• 제25권3호
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• pp.537-547
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• 2001

The paper proposes the fuzzy logic global approximate optimization strategies in optimal sizing of automotive A-pillar trim with rib structures for occupant head protection. Two different strategies referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the inherent nonlinearity in analysis model should be accommodated over the entire design space and the training data is not sufficiently provided. The objective of structural design is to determine the dimensions of rib in A-pillar, minimizing the equivalent head injury criterion HIC(d). The paper describes the head-form modeling and head impact simulation using LS-DYNA3D, and the approximation procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and subsequently presents their generalization capabilities in terms of number of fuzzy rules and training data.

• 전기전자학회논문지
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• 제26권4호
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• pp.736-741
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• 2022

격자 기반 암호화는 최악의 경우를 기반으로 한 강력한 보안, 비교적 효율적인 구현 및 단순성을 누리기 때문에 포스트 양자 암호화 방식 중 가장 실용적인 방식이다. 오류가 있는 링 학습(R-LWE)은 격자 기반 암호화(LBC)의 공개키암호화(Public Key Encryption: PKE) 방식이며, R-LWE의 가장 중요한 연산은 링의 모듈러 다항식 곱셈이다. 본 논문은 R-LWE 암호 시스템의 중간 보안 수준의 매개 변수 집합을 대상으로 하여 근사 컴퓨팅(Approximate Computing: AC) 기술을 기반으로 한 모듈러 곱셈기를 최적화하는 방법을 제안한다. 먼저 복잡한 로직을 간단하게 구현하는 방법으로 LUT을 사용하여 근사 곱셈 연산 중 일부의 연산 과정을 생략하고, 2의 보수 방법을 활용하여 입력 데이터의 값을 이진수로 변환 시 값이 1인 비트의 개수를 최소화하여 필요한 덧셈기의 개수를 절감하는 총 두 가지 방법을 제안한다. 제안된 LUT 기반의 모듈식 곱셈기는 기존 R-LWE 모듈식 곱셈기 대비 속도와 면적 모두 9%까지 줄어들었고, 2의 보수 방법을 적용한 모듈식 곱셈기는 면적을 40%까지 줄이고 속도는 2% 향상되는 것으로 나타났다. 마지막으로 이 두 방법을 모두 적용한 최적화된 모듈식 곱셈기의 면적은 기존대비 43%까지 감소하고 속도는 10%까지 감소하는 것으로 나타났다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
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• pp.386-393
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• 2003

For fast Cholesky factorization, it is most important to reduce the number of non-zero elements by ordering methods. Minimum deficiency ordering produces less non-zero elements. However, since it is very slow. the minimum degree algorithm is widely used. To improve the computation time, Rothberg's AMF uses an approximate deficiency instead of computing the deficiency. In this paper we present simple efficient methods to obtain a good approximate deficiency using information related to cliques. Experimental results show that our proposed method produces better ordering quality than that of AMF.

• Journal of Semiconductor Engineering
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• 제1권1호
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• pp.31-37
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• 2020

We present an approximate DRAM architecture for energy-efficient deep learning. Our key premise is that by bounding memory errors to non-critical information, we can significantly reduce DRAM refresh energy without compromising recognition accuracy of deep neural networks. To validate the key premise, we make extensive Monte-Carlo simulations for several well-known convolutional neural networks such as LeNet, ConvNet and AlexNet with the input of MINIST, CIFAR-10, and ImageNet, respectively. We assume that the highest-order 8-bits (in single precision) and 4-bits (in half precision) are protected from retention errors under the proposed architecture and then, randomly inject bit-errors to unprotected bits with various bit-error-rates. Here, recognition accuracies of the above convolutional neural networks are successfully maintained up to the 10^-5-order bit-error-rate. We simulate DRAM energy during inference of the above convolutional neural networks, where the proposed architecture shows the possibility of considerable energy saving up to 10 ~ 37.5% of total DRAM energy.

• 인터넷정보학회논문지
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• 제14권1호
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• pp.63-69
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• 2013

근접 빈발 패턴 마이닝은 향상된 효율성을 위해 정확한 패턴보다 허용되는 범위 안에서 근접 빈발 패턴을 마이닝한다. 데이터베이스의 크기가 증대함에 따라 거대한 데이터베이스를 처리하기 위해서 더 빠른 마이닝 기법이 필요하게 되고 있다. 또한, 노이지나 데이터의 다양성 때문에 패턴을 마이닝 하는 것에 대한 정확한 결과를 찾기가 더 어렵다. 이러한 경우들에 대해, 근접 빈발 패턴 마이닝을 함으로 실행시간, 메모리 사용량, 그리고 확장성의 관점에서 더 효율적인 마이닝을 수행할 수 있다. 이 논문에서는 확률 기법에 근간한 근접 패턴 마이닝 알고리즘에 대한 특성을 살펴보고 척도가 되는 확률 기법에 기반한 근접 패턴 마이닝 알고리즘에 대해 성능 평가를 한다. 최종적으로 성능의 향상을 위해 테스트 결과를 분석한다.

• 한국철도학회논문집
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• 제16권5호
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• pp.372-378
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• 2013

이 논문에서는 도시철도의 최대수요전력 산출을 위한 근사모델을 제시한다. 전류 벡터 반복법을 활용하여 변전소의 최대수요전력을 계산할 경우에 기존의 방법으로는 수 많은 반복 조류계산이 필요하기 때문에 계산시간 제약으로 인해 실시간 적용이 어렵다는 문제가 있다. 본 논문에서는 모든 조건이 동일한 상태에서 전원 임피던스의 변화에 따른 변전소 최대수요 전력을 빠르게 산출하는 근사모델을 제시한다. 제시된 근사 모델에 의한 산출결과가 기존 모델과 거의 유사한 정확성을 보임을 시뮬레이션을 통해 예시한다.

• IEIE Transactions on Smart Processing and Computing
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• 제6권4호
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• pp.299-304
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• 2017

A wireless passive sensor network is a network consisting of sink nodes, sensor nodes, and radio frequency (RF) sources, where an RF source transfers energy to sensor nodes by radiating RF waves, and a sensor node transmits data by consuming the received energy. Against theoretical expectations, a wireless passive sensor network suffers from many practical difficulties: scarcity of energy, non-simultaneity of energy reception and data transmission, and inefficiency in allocating time resources. Perceiving such difficulties, we propose a simple contending-type medium access control (MAC) scheme for many sensor nodes to deliver packets to a sink node. Then, we derive an approximate expression for the network-wide throughput attained by the proposed MAC scheme. Also, we present an approximate expression for the optimal partition, which maximizes the saturated network-wide throughput. Numerical examples confirm that each of the approximate expressions yields a highly precise value for network-wide throughput and finds an exactly optimal partition.

• 한국소음진동공학회논문집
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• 제24권1호
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• pp.21-27
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• 2014

In this paper, an approximate formula for radiation efficiency of the plate surround by an infinite rigid baffle is studied. The plate is simply supported and one side is in contact with air, while other side with water. By assuming an infinite plate, the fluid loading effect is derived in terms of an effective mass. Based on the observation that the fluid loading effect decreases as frequency increases, the radiation efficiency formula at high frequency, which was originally derived for a plate vibrating in the air, is modified as the approximate formula for a submerged plate. The fluid loading effect is taken into account in the wavenumber of the plate. Comparisons of the approximate formula with the numerical results shows that they match well except the mid-frequency range in which numerical results show many oscillations. In numerically solving the fully coupled equations of motion, fourfold integrals of the impedance coefficients are reduced to single nonsingular integrals, which results in substantial reduction in computing time.